Title: An energy consumption prediction of large public buildings based on data-driven model

Authors: Yongbing Guan; Yebo Fang

Addresses: Engineering Management College, Jinan Engineering Polytechnic, Jinan, Shandong, China ' Department of Municipal Engineering and Equipment, Shandong Urban Construction Vocational College, Jinan, Shandong, China

Abstract: Because the traditional energy consumption prediction method of large public buildings has the problems of large prediction error and long prediction time, an energy consumption prediction of large public buildings based on data-driven model is proposed. The process includes to build the energy consumption model of large public buildings through data driving, collect energy consumption data such as equipment capacity, load grade and equipment failure rate, pre-process the energy consumption data, take the pre-processed energy consumption data as training samples, input it into BP neural network for training, optimise BP neural network by genetic algorithm and build the energy consumption prediction model of large public buildings, and get the prediction results. The simulation results show that the energy consumption prediction method of large public buildings based on data-driven model has short time and good prediction effect.

Keywords: data-driven model; large public buildings; energy consumption prediction; BP neural network; genetic algorithm.

DOI: 10.1504/IJGEI.2023.130682

International Journal of Global Energy Issues, 2023 Vol.45 No.3, pp.207 - 219

Received: 29 Dec 2021
Accepted: 01 Jun 2022
Published online: 02 May 2023 *

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